ExprAlign - the identification of ESTs in non-model species by alignment of cDNA microarray expression profiles

<p>Abstract</p> <p>Background</p> <p>Sequence identification of ESTs from non-model species offers distinct challenges particularly when these species have duplicated genomes and when they are phylogenetically distant from sequenced model organisms. For the common carp, an environmental model of aquacultural interest, large numbers of ESTs remained unidentified using BLAST sequence alignment. We have used the expression profiles from large-scale microarray experiments to suggest gene identities.</p> <p>Results</p> <p>Expression profiles from ~700 cDNA microarrays describing responses of 7 major tissues to multiple environmental stressors were used to define a co-expression landscape. This was based on the Pearsons correlation coefficient relating each gene with all other genes, from which a network description provided clusters of highly correlated genes as 'mountains'. We show that these contain genes with known identities and genes with unknown identities, and that the correlation constitutes evidence of identity in the latter. This procedure has suggested identities to 522 of 2701 unknown carp ESTs sequences. We also discriminate several common carp genes and gene isoforms that were not discriminated by BLAST sequence alignment alone. Precision in identification was substantially improved by use of data from multiple tissues and treatments.</p> <p>Conclusion</p> <p>The detailed analysis of co-expression landscapes is a sensitive technique for suggesting an identity for the large number of BLAST unidentified cDNAs generated in EST projects. It is capable of detecting even subtle changes in expression profiles, and thereby of distinguishing genes with a common BLAST identity into different identities. It benefits from the use of multiple treatments or contrasts, and from the large-scale microarray data.</p

Cycles of heat exposure elevate metabolic enzyme genes and alters digestion in mussels

The intertidal sea mussel Mytilus californianus inhabits the Pacific coastline of North America. As a sessile organism it must cope with daily fluctuations of the marine and terrestrial environments. Organisms in stressful environments are commonly faced with energetic trade-offs between somatic and reproductive growth and stress management. Although, this energetic theory is generally accepted for mussels as well, the spectrum of mechanisms underlying this framework have not been widely investigated. In the current study we hypothesized that mussels acclimated to a cyclical moderately warm aerial environment would display enhanced transcript abundance of genes related to metabolism and exhibit resilient digestive enzyme activity (energy acquisition). Following acclimation to simulated tidal regimes in the laboratory we observed higher gene-expression of citrate synthase (CS), citrate lyase (ACLY), and mammalian target of rapamycin (MTOR) in heat stressed mussels. The expression of CS and MTOR was not elevated under acute thermal stress, suggestive that repeated stress is required for robust expression of these genes given that all other environmental variables are constant. We also observed reduced activity of the digestive enzyme, amylase in heat-shocked acclimated mussels (a proxy for energy acquisition). Our results suggest that mussels that settle high on shore not only face the challenge of thermal stress repair and limited access to food but may also be compromised by reduced digestive performance. Mussels may have adapted to cyclical energetic stress by overexpressing particular energy-related genes that can mitigate the disturbance to energy balance once the abundant transcripts are translated into functional proteins

Molecular Correlates of Social Dominance: A Novel Role for Ependymin in Aggression

Theoretical and empirical studies have sought to explain the formation and maintenance of social relationships within groups. The resulting dominance hierarchies have significant fitness and survival consequences dependent upon social status. We hypothesised that each position or rank within a group has a distinctive brain gene expression profile that correlates with behavioural phenotype. Furthermore, transitions in rank position should determine which genes shift in expression concurrent with the new dominance status. We used a custom cDNA microarray to profile brain transcript expression in a model species, the rainbow trout, which forms tractable linear hierarchies. Dominant, subdominant and submissive individuals had distinctive transcript profiles with 110 gene probes identified using conservative statistical analyses. By removing the dominant, we characterised the changes in transcript expression in sub-dominant individuals that became dominant demonstrating that the molecular transition occurred within 48 hours. A strong, novel candidate gene, ependymin, which was highly expressed in both the transcript and protein in subdominants relative to dominants, was tested further. Using antibody injection to inactivate ependymin in pairs of dominant and subdominant zebrafish, the subdominant fish exhibited a substantial increase in aggression in parallel with an enhanced competitive ability. This is the first study to characterise the molecular signatures of dominance status within groups and the first to implicate ependymin in control of aggressive behaviour. It also provides evidence for indirect genetic effect models in which genotype/phenotype of an individual is influenced by conspecific interactions within a group. The variation in the molecular profile of each individual within a group may offer a new explanation of intraspecific variation in gene expression within undefined groups of animals and provides new candidates for empirical study

Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data

<p>Abstract</p> <p>Background</p> <p>In practice many biological time series measurements, including gene microarrays, are conducted at time points that seem to be interesting in the biologist's opinion and not necessarily at fixed time intervals. In many circumstances we are interested in finding targets that are expressed periodically. To tackle the problems of uneven sampling and unknown type of noise in periodicity detection, we propose to use robust regression.</p> <p>Methods</p> <p>The aim of this paper is to develop a general framework for robust periodicity detection and review and rank different approaches by means of simulations. We also show the results for some real measurement data.</p> <p>Results</p> <p>The simulation results clearly show that when the sampling of time series gets more and more uneven, the methods that assume even sampling become unusable. We find that M-estimation provides a good compromise between robustness and computational efficiency.</p> <p>Conclusion</p> <p>Since uneven sampling occurs often in biological measurements, the robust methods developed in this paper are expected to have many uses. The regression based formulation of the periodicity detection problem easily adapts to non-uniform sampling. Using robust regression helps to reject inconsistently behaving data points.</p> <p>Availability</p> <p>The implementations are currently available for Matlab and will be made available for the users of R as well. More information can be found in the web-supplement <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>.</p

A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay

<p>Abstract</p> <p>Background</p> <p>Patients with a prolonged intensive care unit (ICU) length of stay account for a disproportionate amount of resource use. Early identification of patients at risk for a prolonged length of stay can lead to quality enhancements that reduce ICU stay. This study developed and validated a model that identifies patients at risk for a prolonged ICU stay.</p> <p>Methods</p> <p>We performed a retrospective cohort study of 343,555 admissions to 83 ICUs in 31 U.S. hospitals from 2002-2007. We examined the distribution of ICU length of stay to identify a threshold where clinicians might be concerned about a prolonged stay; this resulted in choosing a 5-day cut-point. From patients remaining in the ICU on day 5 we developed a multivariable regression model that predicted remaining ICU stay. Predictor variables included information gathered at admission, day 1, and ICU day 5. Data from 12,640 admissions during 2002-2005 were used to develop the model, and the remaining 12,904 admissions to internally validate the model. Finally, we used data on 11,903 admissions during 2006-2007 to externally validate the model.</p> <p>Results</p> <p>The variables that had the greatest impact on remaining ICU length of stay were those measured on day 5, not at admission or during day 1. Mechanical ventilation, PaO₂: FiO₂ratio, other physiologic components, and sedation on day 5 accounted for 81.6% of the variation in predicted remaining ICU stay. In the external validation set observed ICU stay was 11.99 days and predicted total ICU stay (5 days + day 5 predicted remaining stay) was 11.62 days, a difference of 8.7 hours. For the same patients, the difference between mean observed and mean predicted ICU stay using the APACHE day 1 model was 149.3 hours. The new model's r²was 20.2% across individuals and 44.3% across units.</p> <p>Conclusions</p> <p>A model that uses patient data from ICU days 1 and 5 accurately predicts a prolonged ICU stay. These predictions are more accurate than those based on ICU day 1 data alone. The model can be used to benchmark ICU performance and to alert physicians to explore care alternatives aimed at reducing ICU stay.</p

Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure.

Numerous genetic loci have been associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP) in Europeans. We now report genome-wide association studies of pulse pressure (PP) and mean arterial pressure (MAP). In discovery (N = 74,064) and follow-up studies (N = 48,607), we identified at genome-wide significance (P = 2.7 × 10(-8) to P = 2.3 × 10(-13)) four new PP loci (at 4q12 near CHIC2, 7q22.3 near PIK3CG, 8q24.12 in NOV and 11q24.3 near ADAMTS8), two new MAP loci (3p21.31 in MAP4 and 10q25.3 near ADRB1) and one locus associated with both of these traits (2q24.3 near FIGN) that has also recently been associated with SBP in east Asians. For three of the new PP loci, the estimated effect for SBP was opposite of that for DBP, in contrast to the majority of common SBP- and DBP-associated variants, which show concordant effects on both traits. These findings suggest new genetic pathways underlying blood pressure variation, some of which may differentially influence SBP and DBP

Oyster Disease Research Program: Building Gene Expression-Based Predictors of Oyster Summer Mortality Syndrome

Pacific oyster (Crassostrea gigas) fisheries on the U.S. West Coast, Mexico, France and Japan, are significantly impacted by high rates of oyster mortality in the summer months, in California ranging from 52-63%. This summer mortality syndrome (SMS) occurs predominantly in near-market ready oysters and seems to impact reproductive females the most. There is an urgent need for studies to investigate the metabolic and molecular basis of the syndrome, with the goal of identifying, and then producing summer mortality-resistant stocks. The specific hypotheses that we are testing is that changes in hemocyte gene expression could be used as a predictor of the onset of summer mortality, and that these changes in gene expression could be used as a predictor of the onset of the syndrome as well as providing insights into the pathophysiological basis of the disease

Oyster Disease Research Program: Building Gene Expression-Based Predictors of Oyster Summer Mortality Syndrome

Pacific oyster (Crassostrea gigas) fisheries on the U.S. West Coast, Mexico, France and Japan, are significantly impacted by high rates of oyster mortality in the summer months, in California ranging from 52-63%. This summer mortality syndrome (SMS) occurs predominantly in near-market ready oysters and seems to impact reproductive females the most. There is an urgent need for studies to investigate the metabolic and molecular basis of the syndrome, with the goal of identifying, and then producing summer mortality-resistant stocks. The specific hypotheses that we are testing is that changes in hemocyte gene expression could be used as a predictor of the onset of summer mortality, and that these changes in gene expression could be used as a predictor of the onset of the syndrome as well as providing insights into the pathophysiological basis of the disease

Gene Expression Predictors of Summer Mortality Syndrome in Pacific Oysters

Native to Japan, the Pacific oyster (Crassostrea gigas) is a major component of the U.S. shellfish industry, hailed as Washington’s most valuable bivalve. NOAA estimates that from 1984 to 2005, oyster farming generated approximately $96.2 million annually (not limited to the Pacific oyster)